Scopus
YÖKSİS DOI Eşleşti
SJR Q1
Prediction of breast cancer using artificial neural networks
Journal of Medical Systems · Ekim 2012
YÖKSİS Kayıtları
Prediction of Breast Cancer Using Artificial Neural Networks
Journal of Medical Systems · 2011 SCI-Expanded 20 atıf
Prof. Dr. İSMAİL SARITAŞ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
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A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network
2012 ISSN: 0148-5598 SCI-Expanded
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A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases
2014 ISSN: 0148-5598 SCI-Expanded Q2
Doç. Dr. MUSTAFA SERTER UZER →
A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm
2011 ISSN: 0148-5598 SCI-Expanded
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Prediction of Breast Cancer Using Artificial Neural Networks
2011 ISSN: 0148-5598 SCI-Expanded 20 atıf Q1
Prof. Dr. İSMAİL SARITAŞ →
Using Elman Recurrent Neural Networks with Conjugate Gradient Algorithm in Determining the Anesthetic the Amount of Anesthetic Medicine to Be Applied
2010 ISSN: 0148-5598 SCI-Expanded
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Design and Construction of a Microcontroller Based Ventilator Synchronized with Pulse Oximeter
2016 ISSN: 0148-5598 SCI-Expanded
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The Use of Stepper Motor-Controlled Proportional Valve for Fio2 Calculation in the Ventilator and its Control with Fuzzy Logic
2017 ISSN: 0148-5598 SCI-Expanded
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An Artificial Neural Network Classification Approach for use the Ultrasound in Physiotherapy
2011 ISSN: 0148-5598 SCI-Expanded
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Estimation of Medicine Amount Used Anesthesia by an Artificial Neural Network
2010 ISSN: 0148-5598 SCI-Expanded 2 atıf
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2018 ISSN: 0148-5598 SCI-Expanded
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Computer-Aided Diagnosis System for Detection of Stomach Cancer with Image Processing Techniques
2019 ISSN: 0148-5598 SCI-Expanded Q1
Doç. Dr. ALİ YAŞAR →
Makale Bilgileri
ISSN01485598
Yayın TarihiEkim 2012
Cilt / Sayfa36 · 2901-2907
Scopus ID2-s2.0-84867299673
Özet
In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians. © 2011 Springer Science+Business Media, LLC.
Yazarlar (1)
1
Ismail Saritas
Anahtar Kelimeler
Artificial neural network
BI-RADS
Breast cancer
Breast cancer prediction
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Medical Systems
Q1
SJR Skoru0,962
H-Index120
YayıncıSpringer New York
ÜlkeUnited States
Information Systems (Q1)
Medicine (miscellaneous) (Q1)
Health Informatics (Q2)
Health Information Management (Q2)
Metrikler
69
Atıf
1
Yazar
4
Anahtar Kelime